Retail Price Optimization Software
Features, Integrations, Benefits, Costs
ScienceSoft applies 16 years of experience in financial software development, 20-year expertise in retail IT, and 34 years in data science to help businesses design and build effective analytics-driven retail price optimization solutions.
Retail Price Optimization Software in a Nutshell
Retail price optimization software enables comprehensive pricing and profitability analytics and introduces data-driven insights on optimal prices for maximized sales margin.
Such solutions help retailers streamline pricing-related decision making and maintain competitive prices across their selling channels.
- Main capabilities:
- Analytics-based optimization of pricing strategies for various products and customer segments.
- Calculating optimal prices across all stages of the product life cycle.
- Calculating optimal discount amounts for promo campaigns.
- Dynamic pricing.
- Essential integrations: CRM, an ecommerce platform, inventory management software, promotions management software, and more.
- Implementation time: 6–10 months for a custom solution.
- Implementation costs: $150,000–$400,000+, depending on the solution complexity.
- Annual ROI: up to 900%+.
Main Approaches to Retail Price Optimization
We at ScienceSoft thoroughly weigh their benefits and limitations for each customer to choose the approach that meets their specific price optimization needs best.
Key Features of Retail Price Optimization Software
ScienceSoft creates price optimization solutions with unique functionality closely bound to our clients’ business objectives. Here, we list the features commonly requested by our customers from the retail sector:
Pricing strategy optimization
- Analyzing available historical and real-time data that may influence pricing strategy optimization decisions:
- Sales data.
- Product profitability.
- Demand seasonality.
- Product promotions.
- Competitors’ prices.
- Stock availability.
- Case-specific types of data (e.g., weather forecast for weather-sensitive items), and more.
- Pricing strategy scenario modeling and profitability analysis of various strategies.
- AI recommendations on optimal pricing strategies (e.g., cost-plus pricing, value-based pricing, penetration pricing, competitive pricing) for particular products, regions, customer segments, etc.
Initial price calculation
- AI-driven reference suggestions for new products (based on the product attributes, such as category, brand, etc.) to use the pricing data of the reference products as a basis for new product pricing.
- Calculation of optimal initial prices and markups for new products.
- Multi-currency price calculation.
- AI suggestions on the features that increase perceived product value (e.g., luxury packaging, free delivery).
- Automated multi-department price approval workflow.
- Calculating price elasticity of demand across various product and customer segments.
- User-defined price segmentation rules for:
- Particular customer segments (e.g., offering lower prices for new customers, customers purchasing online or choosing self-pickup, customers from particular regions).
- Particular products and product segments based on a product’s functionality scope, specific attributes, additional services offering (e.g., charging lower prices for products with simple packaging or non-refundable purchases).
- AI suggestions on differentiated prices for particular customer segments and products/product segments.
Price optimization across the product life cycle
- Product cannibalization analysis to:
- Predict how the launch of new products and retirement of existing products may impact the customer demand across the existing product portfolio.
- Calculate optimal product prices based on the predicted demand changes.
- AI suggestions on the products that require price optimization.
- Calculating optimal product price based on:
- AI-enabled profitability recommendations.
- User-defined price optimization rules (e.g., keeping prices or profit levels within specified thresholds).
- AI-powered recommendations on optimal markdowns throughout the product life cycle.
- Automated price updating across multiple selling platforms (ecommerce websites, customer portals, third-party marketplaces, etc.).
- Real-time monitoring of competitors’ prices and stock availability across relevant public sources (third-party marketplaces, standalone websites, etc.).
- Real-time calculation of optimal prices based on the analysis of product profitability, potential restraints (stock availability), current competitors’ prices and inventory availability, and other factors.
- User-defined triggers for price recalculation, e.g., particular schedules or events, such as user traffic increase, demand spikes, competitors’ price changes, etc.
- Real-time price updating across multiple selling channels.
- AI recommendations on optimal trade promotion events and their timing based on the analysis of their potential to increase demand.
- Profitability analysis to identify the optimal discount amounts (by product or product segment, selling channel, customer segment, region, etc.) taking into account the available promotional budget.
- Automated price recalculation based on the applied discounts.
- Configurable price endings by region or customer segment.
Price personalization (for ecommerce)
- AI suggestions on personalized discounts based on the analysis of customers’ shopping history.
- Real-time analysis of a customer’s shopping cart and recommendations on relevant complementary items that can be added to the cart to get a volume discount.
- Real-time price monitoring across the retailer’s product portfolio (by product, selling channel, region, customer segment, etc.).
- Interactive dashboards enabling side-by-side price comparison to competitors’ prices.
- A full history of product prices.
- Configurable profitability targets (by product, customer segment, selling channel, region, promo campaign, etc.).
- Drill-down dashboards for real-time profitability monitoring against the preset targets.
- Scheduled and ad hoc profitability reports.
Price optimization model management (ML capabilities)
- Configuring numerical and categorical input parameters, rules for data normalization and encoding for the retail price optimization models.
- Real-time monitoring of price optimization model variance (predicted vs. actual demand, sales, etc.).
- Alerts to data scientists on abnormal (poor/superior) model performance.
- Recommendations on model performance improvement (e.g., suggestions on additional case-specific categories of input data).
Key Integrations for Retail Price Optimization Software
ScienceSoft suggests implementing the following integrations to enable fast aggregation of data on pricing factors and ensure seamless flow of analytical results to the relevant systems:
- For data-driven analysis of customers’ purchasing behavior, accurate price adjustment for different customer segments, customer demand forecasting, profitability analysis.
- To share up-to-date pricing data with the sales team.
(e.g., an ecommerce website, a customer portal, a third-party marketplace, POS software)
- To keep prices up-to-date across all selling channels.
- For real-time profitability monitoring against the preset targets, analysis of price elasticity and preferred buying channels across various customer segments.
- For accurate calculation of initial product prices and optimal markups.
An inventory management system
- For data-driven price optimization, planning of discount offers based on inventory availability.
- For facilitated price optimization by product segment.
Product life cycle management software
- To calculate optimal prices for new and existing products.
- To plan an optimal product life cycle, new product launches, and product retirement across product categories.
Promotion management software
- To launch effective promotional campaigns for maximized demand and profitability.
- To calculate optimal discount amounts for promotional events.
Factors that Determine Retail Price Optimization Software Success
Relying on 16 years of experience in implementing financial solutions and 34-year expertise in data analytics, ScienceSoft’s consultants have defined the key factors that help retail companies maximize ROI from price optimization software implementation.
Analysis of versatile data from all the required sources
To optimize prices with full insight into sales data, stock levels, seasonality, competitors’ prices, customer sentiment, and other available pricing and profitability factors.
Smooth integration with business-critical systems
To ensure seamless data flow between the retail price optimization software and a company’s CRM, selling platforms, inventory management software, promotion management software, etc.
A high degree of automation
To free the pricing team from time-consuming manual tasks, such as calculating optimal segmented and personalized prices, updating prices across multiple selling channels, and more.
Focus on security
To protect sensitive pricing data by applying permission-based access control (for pricing analysts, sales and marketing teams, category managers, etc.), pricing data encryption, automated fraud detection, and other security measures.
ScienceSoft’s Head of Data Analytics Department Alex Bekker shares his expertise
"I recommend retail companies to involve professional data scientists to design price optimization models and tune them at the model training stage. It helps employ proper models for various customer and product categories, accurately identify main drivers for product repricing, avoid underpricing and overpricing, etc."
Implementation Costs and Benefits of Retail Price Optimization Software
The costs and timeframes of retail price optimization software implementation vary greatly depending on:
- The number and complexity of a solution’s functional modules (for initial price calculation, discounts optimization, dynamic price management, etc.).
- The implementation of advanced data science technologies (ML, including complex deep learning techniques).
- The volume and structural complexity of pricing-related data used for analytics and training ML models.
- The number and complexity of integrations (with CRM, selling platforms, an inventory management system, procurement software, etc.).
- The number of software users, their roles and specific requirements for the solution’s UX and UI.
- Software availability, performance, security, and scalability requirements.
From ScienceSoft’s experience, a custom price optimization solution of average complexity requires $150,000–$300,000 in investments, while comprehensive pricing optimization software powered with advanced analytics may cost $400,000+.
Price optimization software can bring 900%+ annual ROI for large retail businesses.
Benefits of retail price optimization software
3–15% increase in sales revenue and 1–8% increase in gross margin due to the optimized pricing strategy
4x faster price calculation and 3x faster price updates across various selling channels due to automation
Improved pricing decisions due to analytics-based recommendations on optimal prices and discounts
More competitive prices due to prompt price updating taking into account competitors’ pricing activities
Enhanced visibility of product prices due to real-time price monitoring and a full audit trail of price changes
Off-the-Shelf Retail Price Optimization Software ScienceSoft Recommends
Competera Price Optimization
Segmented pricing optimization for omnichannel retailers.
- AI-powered price setting and optimization based on the historical demand data, competitors’ pricing strategies, profitability targets, and more.
- Calculating optimal prices for various pricing strategies, including cost-plus pricing, competitor-centric pricing, personalized pricing.
- Customizable price optimization rules (e.g., ensuring minimal profitability or price changing for a limited amount).
- Real-time batch product repricing.
- Built-in BI tool for advanced visualization of pricing analytics.
- A comprehensive audit trail of price changes.
- Role-based access control for pricing analysts, sales managers, category managers, merchandisers, and more.
- Competera requires complicated and costly customization to smoothly integrate with legacy corporate systems.
Upon request to a vendor.
PROS Smart Price Optimization and Management
Ecommerce companies that rely on dynamic pricing.
- Real-time price recalculation based on AI-powered analysis of stock availability, customer demand, competitors’ prices, and more.
- Automated price updates across multiple selling channels.
- Configurable price segmentation rules.
- Support for multi-currency price optimization.
- Scenario modeling to analyze the impact of price on the demand, revenue, sales margin.
- Prioritization of price optimization opportunities based on projected sales profitability.
- User-defined price thresholds and exception handling rules.
- Customizable dashboards for real-time profitability tracking.
- To leverage data-science-based price optimization, you’ll need a costly subscription to the ultimate edition of the product.
$6,250+/month for a basic plan, $14,000+/month for an advanced version, custom pricing for the ultimate edition.
Retail discount management and optimization.
- Modeling various types of discounts to forecast their impact on the sales margin.
- AI-powered discount optimization based on customer, product, channel, region, etc.
- Profitability analysis to assess the effectiveness of segment-specific discounts and promo campaigns.
- Rule-based price segmentation.
- User-defined rules to set various types of discounts (e.g., volume discount, advance payment discount).
- Configurable triggers for automated discount price recalculation (cost changes, competitors’ activities, etc.).
- Analytics-based recommendations on optimal personalized prices for various customers and customer segments.
- The product requires substantial customization efforts to meet unique price optimization needs.
Upon request to a vendor.
When to Opt for Custom Retail Price Optimization Software
ScienceSoft recommends retail companies to go for a custom price optimization solution in the following cases:
Implementation of Retail Price Optimization Software with ScienceSoft
In financial software development since 2007 and in data science since 1989, ScienceSoft helps retail companies design and build effective price optimization solutions powered with machine learning, including complex deep learning techniques.
Retail price optimization software consulting
- Analysis of your retail price optimization needs.
- Suggesting optimal retail price optimization solution features (including those powered with AI), architecture, and tech stack.
- Preparing a plan of integration with the existing CRM, selling channels, inventory management software, promotions management software, etc.
- Implementation cost & time estimates, expected ROI calculation.
Retail price optimization software development
- Retail price optimization needs analysis and solution conceptualization.
- Solution architecture design.
- Retail price optimization software development.
- Integrating the solution with existing corporate systems (CRM, selling channels, inventory management software, promotions management software, etc.).
- Quality assurance.
- User training.
- Continuous support and evolution (optional).
ScienceSoft is an international IT consulting and software development company headquartered in McKinney, Texas. We provide consultancy and development services to help retail companies design and build price optimization software. Being ISO 9001 and ISO 27001 certified, we apply a mature quality management system and guarantee that cooperation with us does not pose any risks to our customers’ data security. If you are interested in developing a reliable solution for analytics-driven price optimization, feel free to turn to ScienceSoft’s team.